How are hybrid clouds taking over the hosting environment in 2018?

The days of choosing between public clouds and private clouds are over. Now is the time to choose hybrid clouds that offer the best features of both worlds with neat little price tags and lucrative perks. In the last couple of years, SMBs and start-ups have chosen hybrid cloud technology over private hosting for its cost-effective and resourceful nature. The new generation clouds come with flexible infrastructure and customizable security solutions.

Hybrid clouds aim at the perfect blend of private cloud solutions security and public cloud solution costs. This enables the client websites to remain in secured environments and enjoy SaaS and IaaS facilities. Hybrid cloud solutions have the power and the resources to support the data explosion. In a generation of big data, most IT companies and web solution companies are looking for platforms that can provide them holistic data backup and management facilities. Check out what Upper Saddle River NJ Digital Marketing Agency has to say about the pros of hybrid cloud hosting.

Automation of load balancing

Storage planning for big data is a tremendous driving force behind the increasing demand for hybrid clouds. Most service providers offer scalable storage solutions with complete security for their client websites. This, in turn, helps the new businesses accommodate flexible workloads. Automation, analytics and data backup, everything can run on a demand-driven basis for most hybrid cloud hosting providers. This type of hosting provides responsive data loan balancing that is software-based. Therefore, it can instantly increase or decrease as per demand.

Increased competitive edge and utility-based costs

Enterprises that choose hybrid clouds have reported a decrease in operational and hosting costs over time. The resources these platforms provide often help these companies to expand their horizons and explore new markets. They enjoy better speed and connectivity during peak hours. Automation of cloud resources has helped to speed up has given websites competitive edge over their contemporaries on shared clouds.

Most companies that opt for hybrid hosting solutions report a sharp decrease in operating costs and an increase in customer satisfaction. Almost 56% of all entrepreneurs, who currently use cloud technology services for hosting report seeing a competitive advantage as a result of their choice. They also report a much higher ROI as compared to the private cloud users and shared hosting users.

Reliable services

New businesses need to garner a trustworthy image among the target customers and clients. For this, they need a reliable hosting solution that can offer then next to nil downtime. This should be true even during a disaster. Traditional website hosting called for HDD backups and SSD backups, which were susceptible to natural disasters or human-made crises. Hybrid hosting solutions offer complete cloud integration. The implementation of SaaS and IaaS to your current business operations will allow you to replicate all critical data in a different location. This kind of complete backup solution provides data insurance against all kinds of disasters.

Physical security and cloud security

Security concerns are always present, and currently, they are on the rise thanks to the very real and recent threats ransomware have posed on numerous websites, customers, and regular internet users. Cloud hosting services from hybrid clouds provide enhanced security since the providers store the physical servers within the physical data centers. They enjoy the protection the facility implements to prevent hackers from accessing the files on-site.

In case of business websites using shared clouds, experts can often hold a legit website guilty simply because it shares a platform with a nefarious site. This is a classic case of guilt by association that is sadly still prominent on the web. Google can penalize websites that are simply operating via the same cloud, for sharing platform with another site that indulges in severe black hat techniques.

Provides the latest technologies

From this perspective, it is true that private hosting solutions are the safest since they provided the highest qualities of security and managed to host. Nonetheless, hybrid cloud solutions are also improving over the years, and currently, you will find more than one that promises high-end security measures for all its client websites.

However, when we think about the latest innovative technologies and infrastructures, hybrid cloud systems always take the champion’s trophy. Private systems have their pros, but with hybrid systems, the packages are more flexible. The latter is known to offer the biggest number of opportunities regarding infrastructure for entrepreneurs and website owners.

All business owners, who want the safety of a private cloud, but want to pay the prices of a public cloud, should opt for hybrid infrastructure for their business model.

Source: How are hybrid clouds taking over the hosting environment in 2018? by thomassujain

Sears’ big-data strategy? Just a service call away

If you’d like to see less of your Sears SHLD -0.86% repairman, rest assured, the feeling is mutual.

The venerable (but unprofitable) department store, which is the single largest seller of home appliances in the U.S. and installed 4.5 million of them last year, recently opened a new technology center in Seattle. One of its mandates? Mine data gleaned from the tens of millions of visits that Sears technicians have made to American homes over decades to more effectively diagnose a problem that an air-conditioning unit or dishwasher is having—well before a service call is made.

fiv-07-01-15

That’s right: The Sears repairman, clad in his royal-blue shirt, is as valuable a data vehicle as a cookie stored in your web browser. With 7,000 technicians, Sears is the biggest repair service in the country, visiting 8 million homes a year. Its technicians have catalogued hundreds of millions of records, taking note of the location, model, and make—Sears services a wide array of brands, not just its own 102-year-old Kenmore line—on each visit, so its diagnostic technology can calculate the complexity of a repair as well as a cost and time estimate.

sears_chart

The upside of that data crunching? A reduction in the number of times Sears must dispatch technicians, saving the retailer a nice chunk of change at a time when its sales are flagging, sparing customers a lot of aggravation, and helping it snatch away business from competing repair services. Industrywide, service calls fix the problem on the first visit 75% of the time; Sears’ lofty goal is to get that to a 95% resolution rate. (The company won’t disclose its current rate, saying only that it is above average.)

“How do we leverage the data we have and our digital experience to disrupt a pretty sleepy industry?” asks Arun Arora, a former Staples SPLS -0.13% and Groupon GRPN 0.62% executive who now oversees home appliances and services for Sears. “We’re going to use the digital backbone and data we have that we have not uniquely taken advantage of.”

Its new facility also gives Sears a plum spot in the emerging market for smart home tech and services, something that fits well into CEO Eddie Lampert’s strategy to revive the retailer and reinvent it by turning it into—what else?—more of a technology company.

To read the original article on Fortune, click here.

Originally Posted at: Sears’ big-data strategy? Just a service call away by analyticsweekpick

Oct 18, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Conditional Risk  Source

[ LOCAL EVENTS & SESSIONS]

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[ AnalyticsWeek BYTES]

>> Four levels of Hadoop adoption maturity by analyticsweekpick

>> Data Management Rules for Analytics by analyticsweek

>> Healthcare Analytics Tips for Business Minded Doctors by analyticsweekpick

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[ NEWS BYTES]

>>
 Hitachi Capital partners with Jaywing to improve application credit scores through AI – Finextra Under  Risk Analytics

>>
 Risk Analytics Market Growth Forecast Analysis by Manufacturers, Regions, Type and Application to 2023 – Financial Counselor Under  Risk Analytics

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Antifragile: Things That Gain from Disorder

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[ TIPS & TRICKS OF THE WEEK]

Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.

[ DATA SCIENCE Q&A]

Q:Do you know / used data reduction techniques other than PCA? What do you think of step-wise regression? What kind of step-wise techniques are you familiar with?
A: data reduction techniques other than PCA?:
Partial least squares: like PCR (principal component regression) but chooses the principal components in a supervised way. Gives higher weights to variables that are most strongly related to the response

step-wise regression?
– the choice of predictive variables are carried out using a systematic procedure
– Usually, it takes the form of a sequence of F-tests, t-tests, adjusted R-squared, AIC, BIC
– at any given step, the model is fit using unconstrained least squares
– can get stuck in local optima
– Better: Lasso

step-wise techniques:
– Forward-selection: begin with no variables, adding them when they improve a chosen model comparison criterion
– Backward-selection: begin with all the variables, removing them when it improves a chosen model comparison criterion

Better than reduced data:
Example 1: If all the components have a high variance: which components to discard with a guarantee that there will be no significant loss of the information?
Example 2 (classification):
– One has 2 classes; the within class variance is very high as compared to between class variance
– PCA might discard the very information that separates the two classes

Better than a sample:
– When number of variables is high relative to the number of observations

Source

[ VIDEO OF THE WEEK]

#BigData #BigOpportunity in Big #HR by @MarcRind #JobsOfFuture #Podcast

 #BigData #BigOpportunity in Big #HR by @MarcRind #JobsOfFuture #Podcast

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[ QUOTE OF THE WEEK]

Data is the new science. Big Data holds the answers. – Pat Gelsinger

[ PODCAST OF THE WEEK]

@ChuckRehberg / @TrigentSoftware on Translating Technology to Solve Business Problems #FutureOfData #Podcast

 @ChuckRehberg / @TrigentSoftware on Translating Technology to Solve Business Problems #FutureOfData #Podcast

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[ FACT OF THE WEEK]

100 terabytes of data uploaded daily to Facebook.

Sourced from: Analytics.CLUB #WEB Newsletter

A Check-Up for Artificial Intelligence in the Enterprise

As organizations get ready to invest (or further invest) in AI, some recent research efforts offer insight into what the status quo is around AI in the enterprise and the barriers that could impede adoption. 

According to a recent Teradata study, 80% of IT and business decision-makers have already implemented some form of artificial intelligence (AI) in their business.

The study also found that companies have a desire to increase AI spending. Forty-two percent of respondents to the Teradata study said they thought there was more room for AI implementation across the business, and 30% said their organizations weren’t investing enough in AI.

Forrester recently released their 2018 predictions and also found that firms have an interest investing in AI. Fifty-one percent of their 2017 respondents said their firms were investing in AI, up from 40% in 2016, and 70% of respondents said their firms will have implemented AI within the next 12 months.

While the interest to invest in and grow AI implementation is there, 91% of respondents to the Teradata survey said they expect to see barriers get in the way of investing in and implementing AI.

Forty percent of respondents to the Teradata study said a lack of IT infrastructure was preventing Ai implementation, making it their number one barrier to AI. The second most cited challenge, noted by 30% of Teradata respondents, was lack of access to talent and understanding.

“A lot of the survey results were in alignment with what we’ve experienced with our customers and what we’re seeing across all industries – talent continues to be a challenge in an emerging space,” says Atif Kureishy, Global Vice President of Emerging Practices at Think Big Analytics, a Teradata company.

When it comes to barriers to AI, Kureishy thinks that the greatest obstacles to AI are actually found much farther down the list noted by respondents.

“The biggest challenge [organizations] need to overcome is getting access to data. It’s the seventh barrier [on the list], but it’s the one they need to overcome the most,” says Kureishy.

Kureishy believes that because AI has the eye of the C-suite, organizations are going to find the money and infrastructure and talent. “But you need access to high-quality data, that drives training of these [AI] models,” he says.

Michele Goetz, principal analyst at Forrester and co-author of the Forrester report, “Predictions 2018: The Honeymoon For AI Is Over,” also says that data could be the greatest barrier to AI adoption.

“It all comes down to, how do you make sure you have the right data and you’ve prepared it for your AI algorithm to digest,” she says.

How will companies derive value out of AI? Goetz says in this data and insights-driven business world, companies are looking to use insights to improve experiences with customers. “AI is really recognized by companies as a way to create better relationships and better experiences with their customers,” says Goetz.

One of the most significant findings that came out of the Forrester AI research, says Goetz, is that AI will have a major impact on the way companies think about their business models.

“It is very resource intensive to adopt [AI] without a clear understanding of what [it] is going to do,” says Goetz, “So, you’re seeing there’s more thought going into [the question of] how will this change my business process.”

The Forrester Predictions research also showed that 20% of firms will use AI to make business decisions and prescriptive recommendations for employees and customers. In other words, “machines will get bossy,” says Goetz.

Goetz also says that AI isn’t about replacing employees, it’s about getting more value out of them. “Instead of focusing on drudge work or answering questions that a virtual agent can answer, you can allow those employees to be more creative and think more strategically in the way that they approach tasks.”

And in terms of how you can get a piece of the AI pie? Focus your growth on data engineering skills. Forrester predicts that the data engineer will be the new hot job in 2018.

A Udacity blog post describes data engineers as, “responsible for compiling and installing database systems, writing complex queries, scaling to multiple machines, and putting disaster recovery systems into place.” In essence, they set the data up for data scientists to do the analysis. They also often have a background in software engineering. And according to data gathered in June of 2017 and noted in the Forrester Predictions report, 13% of data-related job postings on Indeed.com were for data engineers, while fewer than 1% were for data scientists.

The post A Check-Up for Artificial Intelligence in the Enterprise appeared first on Think Big.

Originally Posted at: A Check-Up for Artificial Intelligence in the Enterprise by analyticsweekpick

#FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership – Playcast – Data Analytics Leadership Playbook Podcast

* ERRATA (As Reported by Peter: “The book Peter mentioned (at 46:20) by Stuart Russell, “Do the Right Thing”, was published in 2003, and not recently”

In this session Peter Morgan, CEO Deep Learning Partnership sat with Vishal Kumar, CEO AnalyticsWeek and shared his thoughts around Deep Learning, Machine Learning and Artificial Intelligence. They’ve discussed some of the best practices when it comes to picking right solution, right vendor and what are some of the keyword means.

Here’s Peter’s Bio:
Peter Morgan is a scientist-entrepreneur starting out in high energy physics enrolled in the PhD program at the University of Massachusetts at Amherst. After leaving UMass, and founding my own company, Peter has moved into computer networks, designing, implementing and troubleshooting global IP networks for companies such as Cisco, IBM and BT Labs. After getting an MBA and dabbling in financial trading algorithms. Peter has worked for three years on an experiment lead by Stanford University to measure the mass of the neutrino. Since 2012. He had been working in Data Science and Deep Learning, founding an AI Solutions company in Jan 2016.

As an entrepreneur Peter has founded companies in the AI, social media, and music industries. He has also served on the advisory board of technology startups. Peter is a popular speaker at conferences, meetups and webinars. He has cofounded and currently organize meetups in the deep learning space. Peter has business experience in the USA, UK and Europe.

Today, as CEO of Deep Learning Partnership, He leads the strategic direction and business development across product and services. This includes sales and marketing, lead generation, client engagement, recruitment, content creation and platform development. Deep Learning technologies used include computer vision and natural language processing and frameworks like TensorFlow, Keras and MXnet. Deep Learning Partnership design and implement AI solutions for our clients across all business domains.

Podcast is sponsored by:
TAO.ai(https://tao.ai), Artificial Intelligence Driven Career Coach

Originally Posted at: #FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership – Playcast – Data Analytics Leadership Playbook Podcast by v1shal

Oct 11, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Trust the data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> 3 Emerging Big Data Careers in an IoT-Focused World by kmartin

>> Customer Loyalty Resource for Customer Experience Professionals by bobehayes

>> Big Data Insights in Healthcare, Part II. A Perspective on Challenges to Adoption by froliol

Wanna write? Click Here

[ NEWS BYTES]

>>
 Latest technology report on big data security market report explored in latest research – WhaTech Under  Big Data Security

>>
 Snapchat Will Let Media Partners Aggregate, Monetize User Posts – Variety Under  Social Analytics

>>
 How to become a machine learning and AI specialist – Android … – Android Authority (blog) Under  Machine Learning

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Master Statistics with R

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In this Specialization, you will learn to analyze and visualize data in R and created reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform fre… more

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The Industries of the Future

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The New York Times bestseller, from leading innovation expert Alec Ross, a “fascinating vision” (Forbes) of what’s next for the world and how to navigate the changes the future will bring…. more

[ TIPS & TRICKS OF THE WEEK]

Data aids, not replace judgement
Data is a tool and means to help build a consensus to facilitate human decision-making but not replace it. Analysis converts data into information, information via context leads to insight. Insights lead to decision making which ultimately leads to outcomes that brings value. So, data is just the start, context and intuition plays a role.

[ DATA SCIENCE Q&A]

Q:What are confounding variables?
A: * Extraneous variable in a statistical model that correlates directly or inversely with both the dependent and the independent variable
* A spurious relationship is a perceived relationship between an independent variable and a dependent variable that has been estimated incorrectly
* The estimate fails to account for the confounding factor

Source

[ VIDEO OF THE WEEK]

Making sense of unstructured data by turning strings into things

 Making sense of unstructured data by turning strings into things

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[ QUOTE OF THE WEEK]

It is a capital mistake to theorize before one has data. Insensibly, one begins to twist the facts to suit theories, instead of theories to

[ PODCAST OF THE WEEK]

@JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

 @JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

Subscribe 

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[ FACT OF THE WEEK]

Big data is a top business priority and drives enormous opportunity for business improvement. Wikibon’s own study projects that big data will be a $50 billion business by 2017.

Sourced from: Analytics.CLUB #WEB Newsletter

Big Data Explained in Less Than 2 Minutes – To Absolutely Anyone

There are some things that are so big that they have implications for everyone, whether we want them to or not. Big Data is one of those concepts, and is completely transforming the way we do business and is impacting most other parts of our lives.

It’s such an important idea that everyone from your grandma to your CEO needs to have a basic understanding of what it is and why it’s important.

Source for cartoon: click here

What is Big Data?

“Big Data” means different things to different people and there isn’t, and probably never will be, a commonly agreed upon definition out there. But the phenomenon is real and it is producing benefits in so many different areas, so it makes sense for all of us to have a working understanding of the concept.

So here’s my quick and dirty definition:

The basic idea behind the phrase ‘Big Data’ is that everything we do is increasingly leaving a digital trace (or data), which we (and others) can use and analyse. Big Data therefore refers to that data being collected and our ability to make use of it.

I don’t love the term “big data” for a lot of reasons, but it seems we’re stuck with it. It’s basically a ‘stupid’ term for a very real phenomenon – the datafication of our world and our increasing ability to analyze data in a way that was never possible before.

Of course, data collection itself isn’t new. We as humans have been collecting and storing data since as far back as 18,000 BCE. What’s new are the recent technological advances in chip and sensor technology, the Internet, cloud computing, and our ability to store and analyze data that have changed the quantityof data we can collect.

Things that have been a part of everyday life for decades — shopping, listening to music, taking pictures, talking on the phone — now happen more and more wholly or in part in the digital realm, and therefore leave a trail of data.

The other big change is in the kind of data we can analyze. It used to be that data fit neatly into tables and spreadsheets, things like sales figures and wholesale prices and the number of customers that came through the door.

Now data analysts can also look at “unstructured” data like photos, tweets, emails, voice recordings and sensor data to find patterns.

How is it being used?

As with any leap forward in innovation, the tool can be used for good or nefarious purposes. Some people are concerned about privacy, as more and more details of our lives are being recorded and analyzed by businesses, agencies, and governments every day. Those concerns are real and not to be taken lightly, and I believe that best practices, rules, and regulations will evolve alongside the technology to protect individuals.

But the benefits of big data are very real, and truly remarkable.

Most people have some idea that companies are using big data to better understand and target customers. Using big data, retailers can predict what products will sell, telecom companies can predict if and when a customer might switch carriers, and car insurance companies understand how well their customers actually drive.

It’s also used to optimize business processes. Retailers are able to optimize their stock levels based on what’s trending on social media, what people are searching for on the web, or even weather forecasts. Supply chains can be optimized so that delivery drivers use less gas and reach customers faster.

But big data goes way beyond shopping and consumerism. Big data analytics enable us to find new cures and better understand and predict the spread of diseases. Police forces use big data tools to catch criminals and even predict criminal activity and credit card companies use big data analytics it to detect fraudulent transactions. A number of cities are even using big data analytics with the aim of turning themselves into Smart Cities, where a bus would know to wait for a delayed train and where traffic signals predict traffic volumes and operate to minimize jams.

Why is it so important?

The biggest reason big data is important to everyone is that it’s a trend that’s only going to grow.

As the tools to collect and analyze the data become less and less expensive and more and more accessible, we will develop more and more uses for it — everything from smart yoga mats to better healthcare tools and a more effective police force.

And, if you live in the modern world, it’s not something you can escape. Whether you’re all for the benefits big data can bring, or worried about Big Brother, it’s important to be aware of the phenomena and tuned in to how it’s affecting your daily life.

What are your biggest questions about big data? I’d love to hear them in the comments below — and they may inspire future posts to address them.

To read the full article on Data Science Central, click here.

Originally Posted at: Big Data Explained in Less Than 2 Minutes – To Absolutely Anyone

Big big love, how big data’s influencing the future of the online dating scene

Romance and big data have a lot more in common than you might think. Though the world of tech and data may seem an odd place to uncover love, both online dating and big data work to personalise what a person or brand has to offer, matching and targeting it uniquely to appeal to that one special individual who’ll want exactly what’s being advertised.

In many cases for both singles on the dating market and brands on the commercial one, achieving success – using big data to successfully reach the right individual or unique prospect – is the start of (hopefully) lifelong, trusted and better matched relationships, and could be the future for love and online dating.

Data set and match

A facilitator of the modern way of life, big data helps us on a daily basis in all kinds of areas, from retail to healthcare, finance and more. Data is not only relevant to advertisers and marketers however. With infinite uses, targeted information helps us live safer, healthier, more personalised lives – including our love lives – matching the perfect partner to the perfect person at the perfect time. After all, better optimised and analysed data means one in six of all US marriages now occur as a result of online dating.*

Photo courtesy of Pixabay
Photo courtesy of Pixabay

What makes dating data big data?

Defining exactly what makes data ‘big’ data can be confusing.

For example if people simply visit dating sites and input information, this does not constitute big data. Big data is defined by the conjunction of three things; volume, velocity and variability.

From the original computer punch card dating-match systems which first came out in the 60’s,** to the comprehensive online dating sites we have today, the process of using data to pair people has become increasingly streamlined and effective. Higher percentages of people are generating data of volume, variety and velocity online; on dating and social sites and through apps (according to the article “Can Big Data +  Big Dating = True Love?” dating app use is growing faster than all other apps combined). As a result, the data created is allowing matchmaking services to target more accurately than ever before.

Regardless of platform, big data infrastructures already support dating sites and apps**, and make the analysis and management of voluminous data sets (terabytes of information according to eHarmony) possible. In the future, big data will likely play a more active role towards enhancing match accuracy right from the start of data collation, just as in marketing; using real time information about people, their unique background, hobbies and more from a variety of sources in addition to self-inputted information, social profile and mobile app data currently used.

Analysing our secret desires and enhancing profile accuracy

The ability to use data from a varied reach of sources as dating service consumers opt-in, will likely be key in furthering the relationship between big data and dating.

On dating sites users currently input the explicit features they prefer and state what they want to know when looking for a partner, say; commitment views, age, hobbies etc. (Profile creation works on the same principal. But often, our actions don’t quite match our words. For example you may have stated (and think) you’re a fan of pure reggae, but your iTunes history may speak differently. What if in future, purchasing data, on an opt-in basis, could be used to match you more accurately with others with similar tastes?

Analytics, as an informational facilitator already currently uses behavioural matching to similarly enhance accuracy. Users may have stated, and think they prefer brunettes but might actually click on more profiles of redheads without realising. It’s here that data really advances the way online dating works; analysing what you’re actually looking at, and what you’re really looking for best match accuracy.

Helping the course of love run smooth

Ultimately, analysing just what makes people fall for each other is not an exact science, and compatibility on meeting is still the decider for many relationships. So while right now, data can’t quite predict results 100% accurately, it can give romance a helping hand. But give it time – and in a few years data may even be able to solve that.

 

*Data courtesy of Acxiom UK’s whitepaper ‘Searching For Balance In The Use Of Personal Data’

Other references

**Referenced from the Smithsonian.com article – “How Big Data Has Changed Dating”

http://www.smithsonianmag.com/ideas-innovations/How-Big-Data-Has-Ch…

http://readwrite.com/2013/02/14/big-data-big-dating-true-love#awesm…

http://gigaom.com/2011/02/11/okcupid-demystifies-dating-with-big-data/

http://www.zdnet.com/eharmony-translates-big-data-into-love-and-cas…

To read the original article on Data Science Central, click here.

Source

Oct 04, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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statistical anomaly  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> How the lack of the right data affects the promise of big data in India by analyticsweekpick

>> DR. DMITRI WILLIAMS: GAMING ANALYTICS: HOW TO GET THE MOST OUT OF YOUR DATA by analyticsweekpick

>> Apr 12, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

Wanna write? Click Here

[ NEWS BYTES]

>>
 Privacy: Facebook suspends data analytics firm Crimson Hexagon – BetaNews Under  Social Analytics

>>
 Catasys Inc. of Brentwood Reports $4 Million Q2 Loss – Los Angeles Business Journal Under  Health Analytics

>>
 As Nvidia expands in artificial intelligence, Intel defends turf – Reuters Under  Artificial Intelligence

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Probability & Statistics

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This course introduces students to the basic concepts and logic of statistical reasoning and gives the students introductory-level practical ability to choose, generate, and properly interpret appropriate descriptive and… more

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Superintelligence: Paths, Dangers, Strategies

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The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but … more

[ TIPS & TRICKS OF THE WEEK]

Winter is coming, warm your Analytics Club
Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.

[ DATA SCIENCE Q&A]

Q:Explain what resampling methods are and why they are useful?
A: * repeatedly drawing samples from a training set and refitting a model of interest on each sample in order to obtain additional information about the fitted model
* example: repeatedly draw different samples from training data, fit a linear regression to each new sample, and then examine the extent to which the resulting fit differ
* most common are: cross-validation and the bootstrap
* cross-validation: random sampling with no replacement
* bootstrap: random sampling with replacement
* cross-validation: evaluating model performance, model selection (select the appropriate level of flexibility)
* bootstrap: mostly used to quantify the uncertainty associated with a given estimator or statistical learning method

Source

[ VIDEO OF THE WEEK]

@Schmarzo @DellEMC on Ingredients of healthy #DataScience practice #FutureOfData #Podcast

 @Schmarzo @DellEMC on Ingredients of healthy #DataScience practice #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

It is a capital mistake to theorize before one has data. Insensibly, one begins to twist the facts to suit theories, instead of theories to

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with  John Young, @Epsilonmktg

 #BigData @AnalyticsWeek #FutureOfData #Podcast with John Young, @Epsilonmktg

Subscribe 

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[ FACT OF THE WEEK]

Distributed computing (performing computing tasks using a network of computers in the cloud) is very real. Google GOOGL -0.53% uses it every day to involve about 1,000 computers in answering a single search query, which takes no more than 0.2 seconds to complete.

Sourced from: Analytics.CLUB #WEB Newsletter

How the Pharmaceuticals Industry Uses Big Data

Like other sectors using data to transform – including the music industry, professional basketball, beverage manufacturers and even online match-makers – big pharma is into big data.

pharmacy

The pharmaceuticals industry collects massive amounts of data. Estimates are measured in petabytes – tens of billions of records of prescriptions and other transactions, hundreds of millions of patient records, hundreds of thousands of data sources.

How the pharmaceuticals industry uses big data is unlike other industries’ approach, however. Pharma companies use data to fail as soon as they can ­­­– that is, through clinical research trials, pharmas aim to figure out which drugs don’t work as soon possible, so they can focus on developing and marketing those that do.

For these companies, data holds the key to improving…well, everything. In the near term, data determines which drugs move through clinical trials and which populations may be most receptive to new medicines. Longer term, data is critical to fulfilling the promise of personalized medicine based on the genetic profiles of individual patients.

The field of bioinformatics largely reflects the emergence of big data within a biological and scientific context. In addition to bioinformatics, there is a huge opportunity to positively reinforce healthy behaviors through tracking patient outcomes.

 

Imagine what kind of brand loyalty pharmas could achieve if patients could easily access a critical health reading over time such as blood pressure. Seeing those numbers declining over time with the help of a medication could be a powerful message to share.

Interestingly, many stakeholders within the industry – ranging from marketing and innovation teams, to clinical and research staff – now categorize big data broadly in terms of “real-world” and “research” (or clinical) data, as highlighted in this report from Health Affairs.

Research data is collected during the trial process:

Clinical trial data is often collected for the specific purpose of obtaining regulatory approval for a new medicine, or a new indication for a medicine. Clinical trials are rigorously designed, often focused on a highly specific patient population, take significant time to complete, and can be very expensive to complete.

Real-world data, on the other hand is “any data that is not captured within the context of a clinical trial and is not explicitly intended for research purposes.”

Real-world data includes social media posts, which can be incredibly valuable for pharmaceutical companies. Drug companies can supplement clinical findings with the actual experiences of people taking the drugs.

Such “crowdsourced” information about medications can provide insights into surprising side effects or new uses of treatment. These new indications can often become billion-dollar businesses in their own right – as shown by the long history of “happy accidents” in drug development.

Of course, if you have good customer intelligence and know how to capture, manage and analyze a wide range of data, then maybe that’s not an accident. This is similar to what companies in many other industries face – from media and entertainment and telecommunications to financial services.

Pharmas must aggregate data – structured and unstructured – from multiple sources to get a clear view of who their consumers are and what they want. They can also test certain offerings and may discover new uses of existing products.

Undoubtedly, pharmaceuticals face very intense regulatory scrutiny – so matters of data ownership, security and confidentiality are tricky. But it’s interesting that an industry that succeeds largely by failing faster is increasing its customer intelligence through more channels than ever.

To read the original article on Infinitive, click here.

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